使用拉曼光谱和机器学习,早期检测和严重程度分类棉花茎中的垂
Xuanzhang Wang1,2, Jianan Chi1,2, Xiao Zhang1,2
1Country College of Information Engineering, Tarim University, Alar, China.
Frontiers in plant science
|October 17, 2025
概括
在棉花中早期检测Verticillium枯是非常重要的. 拉曼光谱学与机器学习相结合,特别是INFO-SVM模型,可以准确地识别茎中的疾病,使得及时治疗和减少作物损失.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 频谱学是一种光谱学.
背景情况:
- (Verticillium wilt,简称VW) 对棉花生产构成重大威胁.
- 检测大众汽车的传统方法是低效和主观的.
- 拉曼光谱为植物疾病诊断提供了一个快速,非破坏性的替代方案.
研究的目的:
- 开发一种准确和有效的方法,用于早期检测棉花茎中的Verticillium枯.
- 为了评估拉曼光谱的性能与各种数据预处理和机器学习技术相结合.
- 为了确定最佳的模型,将大众汽车归类为棉花.
主要方法:
- 使用拉曼光谱分析了棉花茎.
- 数据预处理涉及萨维茨基-戈莱平滑,缩放和转移,标准正常变量,反向第一阶差异和乘数散射校正.
- 基线校正使用了多项式拟合和自适的代加权惩罚最小平方.
- 使用主要组件分析,连续投影算法和竞争性自适应重量重新抽样来进行特征选择.
- 机器学习模型包括支持向量机器 (SVM) 与INFO,随机森林 (RF) 与PSO,以及长短期内存 (LSTM) 与CSA.
主要成果:
- 使用SG-airPLS-(1/SG) '-CARS预处理的INFO-SVM模型实现了最高的精度.
- 优化的INFO-SVM模型在训练数据上显示了97.5%的准确性和0.974的F1得分.
- 在验证数据上,INFO-SVM模型实现了90.0%的准确性和0.867的F1得分.
- 这种模型的性能优于PSO-RF和CSA-LSTM模型.
结论:
- 拉曼光谱学与优化机器学习相结合,提供了一种准确的方法来分类棉花茎中的Verticillium wilt.
- 这种方法可以早期发现疾病,促进及时干预,并最大限度地减少产量损失.
- 开发的方法在农业疾病管理方面具有重大潜力.
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